Full Description
This book covers novel strategies and state of the art approaches for automated non-invasive systems for early prostate cancer diagnosis. Prostate cancer is the most frequently diagnosed malignancy after skin cancer and the second leading cause of cancer related male deaths in the USA after lung cancer. However, early detection of prostate cancer increases chances of patients' survival. Generally, The CAD systems analyze the prostate images in three steps: (i) prostate segmentation; (ii) Prostate description or feature extraction; and (iii) classification of the prostate status.
Explores all of the latest research and developments in state-of-the art imaging of the prostate from world class experts.
Contains a comprehensive overview of 2D/3D Shape Modeling for MRI data.
Presents a detailed examination of automated segmentation of the prostate in 3D imaging.
Examines Computer-Aided-Diagnosis through automated techniques.
There will be extensive references at the end of each chapter to enhance further study.
Contents
Preface
Acknowledgements
Editors
Contributors
1. History of Imaging for Prostate Cancer
Sutchin R. Patel
2. Transrectal Ultrasound (TRUS)-Guided Prostate Biopsy: Historical Perspective and Contemporary Clinical Application
Jennifer Fantasia, Dragan Golijanin, and Boris Gershman
3. Current Active Surveillance Protocol for Prostate Cancer
Scott Greenberg and Jennifer Yates
4. Prostate MRI
J. Pereira, Gyan Pareek, and D. Grand
5. Current Role and Evolution of MRI Fusion Biopsy for Prostate Cancer
Danielle Velez, Joseph Brito, and Joseph Renzulli II
6. Current Role of Focal Therapy for Prostate Cancer
H. Abraham Chiang and George E. Haleblian
7. High-Intensity Focused Ultrasound (HIFU)
Rutveej Patel and Sammy Elsamra
8. Current Role of Cryotherapy in the Treatment of Prostate Cancer
Adnan Dervishi and Murali K. Ankem
9. Transperineal Mapping of the Prostate for Biopsy Strategies
Daniel Kaplon and Winston Barzell
10. Computer-Aided Diagnosis Systems for Prostate Cancer Detection: Challenges and Methodologies
Guillaume Lemaître, Robert Martí, and Fabrice Meriaudeau
11. Early Diagnosis and Staging of Prostate Cancer Using Magnetic Resonance Imaging: State of the Art and Perspectives
Ruba Alkadi, Fatma Taher, Ayman El-Baz, and Naoufel Werghi
12. A DCE-MRI-Based Noninvasive CAD System for Prostate Cancer Diagnosis
F. Khalifa, A. Shalaby, Mohamed Abou El-Ghar, Jasjit S. Suri, and A. El-Baz
13. Prostate Segmentation from DW-MRI Using Level-Set Guided by Nonnegative Matrix Factorization
Islam Reda, Patrick McClure, Ahmed Shalaby, Mohammed Elmogy, Ahmed Aboulfotouh, Mohamed Abou El-Ghar, Moumen El-Melegy, Jasjit S. Suri, and Ayman El-Baz
14. Automated Prostate Image Recognition and Segmentation
Ke Yan, Xiuying Wang, Jinman Kim, Changyang Li, Dagan Feng, and Mohamed Khadra
15. Precision Imaging of Prostate Cancer: Computer-Aided Detection and Their Clinical Applications
Baowei Fei
16. Computer-Aided Diagnosis of Prostate Magnetic Resonance Imaging: From Bench to Bedside
Valentina Giannini, Simone Mazzetti, Filippo Russo, and Daniele Regge
17. Magnetic Resonance Imaging in the Detection of Prostate Cancer
Timothy D. McClure, Daniel Margolis, and Peter N. Schlegel
18. Diagnosing Prostate Cancer Based on Deep Learning with a Stacked Nonnegativity Constraint Autoencoder
Islam Reda, Ahmed Shalaby, Mohammed Elmogy, Ahmed Aboulfotouh, Mohamed Abou El-Ghar, Adel Elmagharaby, and Ayman El-Baz
19. MRI Imaging of Seminal Vesicle Invasion (SVI) in Prostate Adenocarcinoma
Samuel A. Gold, Graham R. Hale, Kareem N. Rayn, Vladimir Valera, Jonathan B. Bloom, and Peter A. Pinto
Index
